How to interact with and consume web services in R with mrsdeploy

Article
07/12/2022

Important

This content is being retired and may not be updated in the future. The support for Machine Learning Server will end on July 1, 2022. For more information, see What's happening to Machine Learning Server?

Applies to: Machine Learning Server, Microsoft R Server 9.x

After a web service has been published or updated, any authenticated user can list, examine, and consume that web service. You can do so directly in R using the functions in the mrsdeploy R package. The mrsdeploy R package is installed with both Machine Learning Server and Microsoft R Client. Also note that application developers can also consume a web service in the language of their choice via Swagger.

If you do not want to list, examine, or consume the web service in R, a set of RESTful APIs are also available to provide direct programmatic access to a service's lifecycle directly.

To list, examine, or consume the web service outside of R, use the RESTful APIs, which provide direct programmatic access to a service's lifecycle.

Requirements

Before you can use the functions in the mrsdeploy R package to manage your web services, you must:

Have access to a Machine Learning Server instance that was properly configured to host web services.
Authenticate with Machine Learning Server using the remoteLogin() or remoteLoginAAD() functions in the mrsdeploy package as described in the article "Connecting to Machine Learning Server to use mrsdeploy."

Find and list web services

Any authenticated user can retrieve a list of web services using the listServices() function in the mrsdeploy package.

Use function arguments to return a specific web service or all labeled versions of a given web service. See the package reference help page for listServices() for the full description of all arguments.

Your ability to see the code inside the web service depends on your permissions. Did you publish the web service or do you have the "Owner" role?

If yes, then the code in the service is returned along with other metadata.
If no, then the code in the service is never returned in the metadata.

To learn more about the roles in your organization, contact your Machine Learning Server administrator.

Function	Response	R Function Help
listServices(...)	R list containing service metadata.	View

Once you find the service you want, use the getService() function to retrieve the service object for consumption.

Example code:

# Return metadata for all services hosted on this server
serviceAll <- listServices()

# Return metadata for all versions of service "mtService" 
mtServiceAll <- listServices("mtService")

# Return metadata for version "v1" of service "mtService" 
mtService <- listServices("mtService", "v1")

# View service capabilities/schema for mtService v1. 
# For example, the input schema:
#   list(name = "wt", type = "numeric")
#   list(name = "dist", type = "numeric")
print(mtService)

For a detailed example with listServices, check out these "Workflow" examples.

Example output:

R Server 9.1+	R Server 9.0

Retrieve and examine service objects

Any authenticated user can retrieve a web service object for consumption. After the object is returned, you can look at its capabilities to see what the service can do and how it should be consumed.

After you've authenticated, use the getService() function in the mrsdeploy package to retrieve a service object. See the package reference help page for getService() for the full description of all arguments.

Function	Response	R Function Help
getService(...)	Returns an API instance client stub for consuming that service and viewing its service holdings) as an R6 class.	View

Example:

# Get service using getService() function from `mrsdeploy` package.
# Assign service to the variable `api`.
api <- getService("mtService", "v1.0.0")

# Print capabilities to see what service can do.
print(api$capabilities())
     
# Start interacting with the service, for example:
# Calling the function, `manualTransmission`
# contained in this service.
result <- api$manualTransmission(120, 2.8)

For a detailed example with getService, check out these "Workflow" examples.

Interact with API clients

When you publish, update, or get a web service, an API instance is returned as an R6 class. This instance is a client stub you can use to consume that service and view its service holdings.

You can use the following supported public functions to interact with the API client instance.

Function	Description
print	Print method that lists all members of the object
capabilities	Report on the service features such as I/O schema, name, version
consume	Consume the service based on I/O schema
consume alias	Alias to the consume function for convenience (see alias argument for the publishService function).
swagger	Displays the service's swagger specification
batch	Define the data records to be batched. There are additional publish functions used to consume a service asynchronously via batch execution.

Example:

# Get service using getService() function from `mrsdeploy`.
# Assign service to the variable `api`
api <- getService("mtService", "v1.0.0")

# Print capabilities to see what service can do.
print(api)

# Print the service name, version, inputs, outputs, and the
# Swagger-based JSON file used to consume the service 
cap <- api$capabilities()
print(cap$name)
print(cap$version)
print(cap$inputs)
print(cap$outputs)
print(cap$swagger)

# Start interacting with the service by calling it with the
# generic name `consume` based on I/O schema
result <- api$consume(120, 2.8)

# Or, start interacting with the service using the alias argument
# that was defined at publication time.
result <- api$manualTransmission(120, 2.8)

# Since you're authenticated, get this service's `swagger.json`.
swagger <- api$swagger(json = FALSE)
cat(swagger)

For a detailed example, check out these "Workflow" examples.

Consume web services

Web services are published to facilitate the consumption and integration of the operationalized models and code. Whenever the web service is published or updated, a Swagger-based JSON file is generated automatically that define the service.

When you publish a service, you can let people know that it is ready for consumption. Users can get the Swagger file they need to consume the service directly in R or via the API. To make it easy for others to find your service, provide them with the service name and version number (or they can use the listServices() function).

Users can consume the service directly using a single consumption call. This approach is referred to as a "Request Response" approach and is described in the following section. Another approach is the asynchronous "Batch" consumption approach, where users send as a single request to Machine Learning Server, which then makes multiple asynchronous API calls on your behalf.

Collaborate with data scientists

Other data scientists may want to explore, test, and consume Web services directly in R using the functions in the mrsdeploy package. Quality engineers might want to bring the models in these web services into validation and monitoring cycles.

You can share the name and version of a web service with fellow data scientists so they can call that service in R using the functions in the mrsdeploy package. After authenticating, data scientists can use the getService() function in R to call the service. Then, they can get details about the service and start consuming it.

You can also build a client library directly in R using the httr package.

Note

It is also possible to perform batch consumption as described here.

In this example, replace the following remoteLogin() function with the correct login details for your configuration. Connecting to Machine Learning Server using the mrsdeploy package is covered in this article.

##########################################################################
#      Perform Request-Response Consumption & Get Swagger Back in R      #
##########################################################################

# Use `remoteLogin` to authenticate with the server using the local admin 
# account. Use session = false so no remote R session started
remoteLogin("http://localhost:12800", 
            username = “admin”, 
            password = “{{YOUR_PASSWORD}}”,
            session = FALSE)
   
# Get service using getService() function from `mrsdeploy`
# Assign service to the variable `api`.
api <- getService("mtService", "v1.0.0")

# Print capabilities to see what service can do.
print(api$capabilities())

# Start interacting with the service, for example:
# Calling the function, `manualTransmission` contained in this service.
result <- api$manualTransmission(120, 2.8)

# Print response output named `answer`
print(result$output("answer")) # 0.6418125  

# Since you're authenticated now, get `swagger.json`.
swagger <- api$swagger()
cat(swagger, file = "swagger.json", append = FALSE)

Collaborate with application developers

Application developers can call and integrate a web service into their applications using the service-specific Swagger-based JSON file and by providing any required inputs to that service.

Using the Swagger-based JSON file, application developers can generate client libraries for integration. Read "How to integrate web services and authentication into your application" for more details.

Application developers can get the Swagger-based JSON file in one of these ways:

A data scientist, probably the one who published the service, can send you the Swagger-based JSON file. This approach is often faster than the following one. They can get the file in R with the following code and send it to the application developer:
```
api <- getService("<name>", "<version>")
swagger <- api$swagger()
cat(swagger, file = "swagger.json", append = FALSE) 
```
Or, the application developer can request the file as an authenticated user with an active bearer token in the request header (since all API calls must be authenticated). The URL is formed as follows:
```
GET /api/<service-name>/<service-version>/swagger.json
```

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